论文标题
通过无对齐的无晶格MMI检测唤醒单词检测
Wake Word Detection with Alignment-Free Lattice-Free MMI
论文作者
论文摘要
始终在语言界面，例如个人数字助理，依靠一个唤醒词开始处理口语输入。我们提出了从部分标记的培训数据中训练混合DNN/HMM唤醒单词检测系统的新方法，并将其用于在线应用中：（i）我们删除了LF-MMI培训算法中帧级对准的先决条件（ii）我们表明，经典的关键字/填充模型必须为良好性能的明确非语音（沉默）模型补充； （iii）我们提出了一个基于FST的解码器，以执行在线检测。我们在两个真实数据集上评估了我们的方法，显示50％-90％的错误拒绝率以预先指定的虚假警报率与以前发表的最佳数字相比，并将它们重新验证为第三（大）数据集。
Always-on spoken language interfaces, e.g. personal digital assistants, rely on a wake word to start processing spoken input. We present novel methods to train a hybrid DNN/HMM wake word detection system from partially labeled training data, and to use it in on-line applications: (i) we remove the prerequisite of frame-level alignments in the LF-MMI training algorithm, permitting the use of un-transcribed training examples that are annotated only for the presence/absence of the wake word; (ii) we show that the classical keyword/filler model must be supplemented with an explicit non-speech (silence) model for good performance; (iii) we present an FST-based decoder to perform online detection. We evaluate our methods on two real data sets, showing 50%--90% reduction in false rejection rates at pre-specified false alarm rates over the best previously published figures, and re-validate them on a third (large) data set.
